A large language model (LLM) is used to train artificial intelligence (AI) to understand and generate text just like a human. Learn more about large language models, their popular applications, and how LLMs differ from other computer learning models.

Large Language Models Explained

Large language models are deep learning algorithms designed to train AI programs. LLMs are a type of transformer model or a neural network that looks for patterns in sequential data sets (like words in a sentence) to establish context. The algorithm outputs an appropriate, human-like response when presented with a text prompt.

The most popular applications of LLMs are AI chatbots. Examples of large language models include GPT-4, which powers the popular ChatGPT, and PaLM2, the algorithm behind Google Gemini. They truly live up to their name: LLMs are typically so massive that they can't run on one computer, so they operate as a web service rather than a standalone program.

How Do LLMs Work?

Transformer models are made up of layers that can be stacked to create increasingly complex algorithms. LLMs, in particular, rely on two key features of transformer models: positional encoding and self-attention. 

Positional encoding allows the model to analyze text non-sequentially to identify patterns. Self-attention assigns a weight to each input that determines its importance compared to the rest of the data. That way, the model can pick out the most important parts in large amounts of text.

Through extensive unsupervised learning, LLMs can reliably predict the next word in a sentence based on the rules of grammar in human languages. Grammatical rules are not preprogrammed into a large language model; the algorithm infers grammar as it reads text.

Uses of LLMs

While virtual assistants like Alexa and Siri can respond to a list of predetermined commands with pre-programmed answers, LLMs analyze substantial text inputs (entire paragraphs or even longer) and provide unique, cohesive, and creative responses.

With the help of LLMs, artificial intelligence programs can perform the following tasks:

• Content generation: Writing stories, poems, scripts, and marketing materials
• Summarization: Compiling notes or transcripts for meetings
• Translation: Interpreting between human languages as well as computer languages
• Classification: Generating lists and analyzing text for tone (positive, negative, or neutral)
  

While AI chatbots are especially helpful in customer service, LLMs have promising applications in several fields, from engineering to healthcare. For example, LLMs can analyze research papers, health records, and other data to develop new medical treatments.

LLM Pros and Cons

Because LLM algorithms learn language by identifying relationships between words, they are not limited to one human language. Likewise, LLMs don't need to be trained for any specific skill. Thus, LLMs have a lot of flexibility in understanding the nuances of human language.

On the other hand, LLMs require a lot of test data before they can be useful. For example, GPT-4 was trained using books, articles, and other text available on the internet before it was released to the public.

Training an LLM requires a lot of time and computational resources, resulting in high power bills. Even though the learning process is unsupervised, human expertise is still needed to develop and maintain LLMs. The large amount of data necessary to train LLMs also presents a challenge, especially when dealing with sensitive information like health or financial data.